LongComBat QC: multi-pipeline report
knitr::opts_chunk$set(
echo = FALSE, message = FALSE, warning = FALSE,
fig.width = 7, fig.height = 4, fig.retina = 2
)
options(knitr.kable.NA = "")
suppressPackageStartupMessages({
pacman::p_load(dplyr, tidyr, ggplot2, tibble, knitr, kableExtra)
})
root <- ".../projects/abcd-projs/smri-pub-abcd/"
source("longComBat-pub-sMRI-abcd.R")
log_file <- "render_progress.log"
writeLines("", log_file)pipelines
thickness — f
[longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6
dataset: thickness | sex: f | region: smri_thick_cdk_cdmdfrlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -11028.53 | -11001.73 | 5518.266 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -11101.46 | -11047.86 | 5558.731 | 1 vs 2 | 80.93027 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -9208.909 | -9182.108 | 4608.454 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -9200.964 | -9147.363 | 4608.482 | 1 vs 2 | 0.0555966 | 0.9996207 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 2.813 | 0.118 |
| brain_metric | scanner2 | 1720 | 2.845 | 0.137 |
| brain_metric | scanner3 | 204 | 2.780 | 0.118 |
| brain_metric | scanner5 | 1596 | 2.854 | 0.115 |
| brain_metric | scanner6 | 2015 | 2.859 | 0.119 |
| brain_metric.combat | scanner1 | 469 | 2.854 | 0.127 |
| brain_metric.combat | scanner2 | 1720 | 2.848 | 0.136 |
| brain_metric.combat | scanner3 | 204 | 2.844 | 0.122 |
| brain_metric.combat | scanner5 | 1596 | 2.842 | 0.133 |
| brain_metric.combat | scanner6 | 2015 | 2.850 | 0.136 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -0.023 | |||
| age_slope | -0.023 | |||
| scanner:scanner3 | 2.783 | 2.847 | 0.064 | 2.297 |
| scanner:scanner1 | 2.806 | 2.847 | 0.041 | 1.474 |
| scanner:scanner5 | 2.859 | 2.847 | -0.012 | -0.417 |
| scanner:scanner6 | 2.857 | 2.848 | -0.009 | -0.310 |
| scanner:scanner2 | 2.845 | 2.848 | 0.003 | 0.090 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: thickness | sex: f | region: smri_thick_cdk_cdmdfrlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -11028.53 | -11001.73 | 5518.266 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -11101.46 | -11047.86 | 5558.731 | 1 vs 2 | 80.93027 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -9210.049 | -9183.248 | 4609.024 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -9202.094 | -9148.493 | 4609.047 | 1 vs 2 | 0.045162 | 0.9997489 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 2.813 | 0.118 |
| brain_metric | scanner2 | 1720 | 2.845 | 0.137 |
| brain_metric | scanner3 | 204 | 2.780 | 0.118 |
| brain_metric | scanner5 | 1596 | 2.854 | 0.115 |
| brain_metric | scanner6 | 2015 | 2.859 | 0.119 |
| brain_metric.combat | scanner1 | 469 | 2.854 | 0.127 |
| brain_metric.combat | scanner2 | 1720 | 2.847 | 0.136 |
| brain_metric.combat | scanner3 | 204 | 2.843 | 0.122 |
| brain_metric.combat | scanner5 | 1596 | 2.842 | 0.133 |
| brain_metric.combat | scanner6 | 2015 | 2.850 | 0.136 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -0.023 | |||
| age_slope | -0.023 | |||
| scanner:scanner3 | 2.783 | 2.846 | 0.063 | 2.268 |
| scanner:scanner1 | 2.806 | 2.848 | 0.042 | 1.490 |
| scanner:scanner5 | 2.859 | 2.847 | -0.012 | -0.410 |
| scanner:scanner6 | 2.857 | 2.848 | -0.009 | -0.314 |
| scanner:scanner2 | 2.845 | 2.848 | 0.002 | 0.087 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: thickness | sex: f | region: smri_thick_cdk_cdmdfrlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -11028.53 | -11001.73 | 5518.266 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -11101.46 | -11047.86 | 5558.731 | 1 vs 2 | 80.93027 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -9208.994 | -9182.193 | 4608.497 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -9201.053 | -9147.452 | 4608.527 | 1 vs 2 | 0.059926 | 0.99956 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 2.813 | 0.118 |
| brain_metric | scanner2 | 1720 | 2.845 | 0.137 |
| brain_metric | scanner3 | 204 | 2.780 | 0.118 |
| brain_metric | scanner5 | 1596 | 2.854 | 0.115 |
| brain_metric | scanner6 | 2015 | 2.859 | 0.119 |
| brain_metric.combat | scanner1 | 469 | 2.854 | 0.127 |
| brain_metric.combat | scanner2 | 1720 | 2.848 | 0.136 |
| brain_metric.combat | scanner3 | 204 | 2.843 | 0.122 |
| brain_metric.combat | scanner5 | 1596 | 2.842 | 0.133 |
| brain_metric.combat | scanner6 | 2015 | 2.850 | 0.136 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -0.023 | |||
| age_slope | -0.023 | |||
| scanner:scanner3 | 2.783 | 2.846 | 0.064 | 2.284 |
| scanner:scanner1 | 2.806 | 2.848 | 0.042 | 1.484 |
| scanner:scanner5 | 2.859 | 2.847 | -0.012 | -0.417 |
| scanner:scanner6 | 2.857 | 2.848 | -0.009 | -0.310 |
| scanner:scanner2 | 2.845 | 2.848 | 0.003 | 0.088 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7
dataset: thickness | sex: f | region: smri_thick_cdk_cdmdfrlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -11028.53 | -11001.73 | 5518.266 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -11101.46 | -11047.86 | 5558.731 | 1 vs 2 | 80.93027 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -9212.788 | -9185.987 | 4610.394 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -9204.859 | -9151.258 | 4610.430 | 1 vs 2 | 0.0714682 | 0.9993765 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 2.813 | 0.118 |
| brain_metric | scanner2 | 1720 | 2.845 | 0.137 |
| brain_metric | scanner3 | 204 | 2.780 | 0.118 |
| brain_metric | scanner5 | 1596 | 2.854 | 0.115 |
| brain_metric | scanner6 | 2015 | 2.859 | 0.119 |
| brain_metric.combat | scanner1 | 469 | 2.854 | 0.127 |
| brain_metric.combat | scanner2 | 1720 | 2.848 | 0.136 |
| brain_metric.combat | scanner3 | 204 | 2.843 | 0.122 |
| brain_metric.combat | scanner5 | 1596 | 2.842 | 0.133 |
| brain_metric.combat | scanner6 | 2015 | 2.850 | 0.136 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -0.023 | |||
| age_slope | -0.023 | |||
| scanner:scanner3 | 2.783 | 2.846 | 0.063 | 2.266 |
| scanner:scanner1 | 2.806 | 2.848 | 0.042 | 1.485 |
| scanner:scanner5 | 2.859 | 2.847 | -0.012 | -0.416 |
| scanner:scanner6 | 2.857 | 2.848 | -0.009 | -0.309 |
| scanner:scanner2 | 2.845 | 2.848 | 0.003 | 0.089 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7
dataset: thickness | sex: f | region: smri_thick_cdk_cdmdfrlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -11028.53 | -11001.73 | 5518.266 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -11101.46 | -11047.86 | 5558.731 | 1 vs 2 | 80.93027 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -9204.983 | -9178.182 | 4606.491 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -9197.041 | -9143.440 | 4606.521 | 1 vs 2 | 0.0587585 | 0.9995768 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 2.813 | 0.118 |
| brain_metric | scanner2 | 1720 | 2.845 | 0.137 |
| brain_metric | scanner3 | 204 | 2.780 | 0.118 |
| brain_metric | scanner5 | 1596 | 2.854 | 0.115 |
| brain_metric | scanner6 | 2015 | 2.859 | 0.119 |
| brain_metric.combat | scanner1 | 469 | 2.854 | 0.127 |
| brain_metric.combat | scanner2 | 1720 | 2.847 | 0.136 |
| brain_metric.combat | scanner3 | 204 | 2.843 | 0.122 |
| brain_metric.combat | scanner5 | 1596 | 2.842 | 0.133 |
| brain_metric.combat | scanner6 | 2015 | 2.850 | 0.136 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -0.023 | |||
| age_slope | -0.023 | |||
| scanner:scanner3 | 2.783 | 2.846 | 0.064 | 2.282 |
| scanner:scanner1 | 2.806 | 2.848 | 0.042 | 1.484 |
| scanner:scanner5 | 2.859 | 2.847 | -0.012 | -0.416 |
| scanner:scanner6 | 2.857 | 2.848 | -0.009 | -0.310 |
| scanner:scanner2 | 2.845 | 2.848 | 0.002 | 0.088 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
thickness — m
[longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6
dataset: thickness | sex: m | region: smri_thick_cdk_cdmdfrlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -12255.3 | -12227.97 | 6131.651 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -12363.7 | -12309.03 | 6189.848 | 1 vs 2 | 116.3952 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -10226.99 | -10199.65 | 5117.495 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -10219.05 | -10164.38 | 5117.524 | 1 vs 2 | 0.0585278 | 0.9995801 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 2.779 | 0.132 |
| brain_metric | scanner2 | 1808 | 2.829 | 0.144 |
| brain_metric | scanner3 | 196 | 2.756 | 0.129 |
| brain_metric | scanner5 | 2016 | 2.841 | 0.111 |
| brain_metric | scanner6 | 2264 | 2.848 | 0.121 |
| brain_metric.combat | scanner1 | 579 | 2.833 | 0.138 |
| brain_metric.combat | scanner2 | 1808 | 2.835 | 0.141 |
| brain_metric.combat | scanner3 | 196 | 2.819 | 0.133 |
| brain_metric.combat | scanner5 | 2016 | 2.828 | 0.131 |
| brain_metric.combat | scanner6 | 2264 | 2.834 | 0.137 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -0.014 | |||
| age_slope | -0.014 | |||
| scanner:scanner3 | 2.769 | 2.829 | 0.060 | 2.163 |
| scanner:scanner1 | 2.777 | 2.832 | 0.055 | 1.975 |
| scanner:scanner6 | 2.844 | 2.831 | -0.014 | -0.477 |
| scanner:scanner5 | 2.844 | 2.831 | -0.012 | -0.432 |
| scanner:scanner2 | 2.825 | 2.831 | 0.007 | 0.237 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: thickness | sex: m | region: smri_thick_cdk_cdmdfrlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -12255.3 | -12227.97 | 6131.651 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -12363.7 | -12309.03 | 6189.848 | 1 vs 2 | 116.3952 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -10228.03 | -10200.69 | 5118.014 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -10220.18 | -10165.51 | 5118.090 | 1 vs 2 | 0.1534112 | 0.9972043 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 2.779 | 0.132 |
| brain_metric | scanner2 | 1808 | 2.829 | 0.144 |
| brain_metric | scanner3 | 196 | 2.756 | 0.129 |
| brain_metric | scanner5 | 2016 | 2.841 | 0.111 |
| brain_metric | scanner6 | 2264 | 2.848 | 0.121 |
| brain_metric.combat | scanner1 | 579 | 2.834 | 0.138 |
| brain_metric.combat | scanner2 | 1808 | 2.835 | 0.141 |
| brain_metric.combat | scanner3 | 196 | 2.817 | 0.133 |
| brain_metric.combat | scanner5 | 2016 | 2.828 | 0.131 |
| brain_metric.combat | scanner6 | 2264 | 2.834 | 0.137 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -0.014 | |||
| age_slope | -0.014 | |||
| scanner:scanner3 | 2.769 | 2.827 | 0.058 | 2.110 |
| scanner:scanner1 | 2.777 | 2.832 | 0.056 | 2.001 |
| scanner:scanner6 | 2.844 | 2.831 | -0.014 | -0.480 |
| scanner:scanner5 | 2.844 | 2.831 | -0.012 | -0.427 |
| scanner:scanner2 | 2.825 | 2.831 | 0.007 | 0.234 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: thickness | sex: m | region: smri_thick_cdk_cdmdfrlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -12255.3 | -12227.97 | 6131.651 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -12363.7 | -12309.03 | 6189.848 | 1 vs 2 | 116.3952 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -10226.93 | -10199.59 | 5117.465 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -10219.00 | -10164.32 | 5117.498 | 1 vs 2 | 0.0666898 | 0.9994563 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 2.779 | 0.132 |
| brain_metric | scanner2 | 1808 | 2.829 | 0.144 |
| brain_metric | scanner3 | 196 | 2.756 | 0.129 |
| brain_metric | scanner5 | 2016 | 2.841 | 0.111 |
| brain_metric | scanner6 | 2264 | 2.848 | 0.121 |
| brain_metric.combat | scanner1 | 579 | 2.833 | 0.138 |
| brain_metric.combat | scanner2 | 1808 | 2.835 | 0.141 |
| brain_metric.combat | scanner3 | 196 | 2.819 | 0.133 |
| brain_metric.combat | scanner5 | 2016 | 2.828 | 0.131 |
| brain_metric.combat | scanner6 | 2264 | 2.834 | 0.137 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -0.014 | |||
| age_slope | -0.014 | |||
| scanner:scanner3 | 2.769 | 2.829 | 0.060 | 2.161 |
| scanner:scanner1 | 2.777 | 2.832 | 0.055 | 1.977 |
| scanner:scanner6 | 2.844 | 2.831 | -0.014 | -0.479 |
| scanner:scanner5 | 2.844 | 2.831 | -0.012 | -0.427 |
| scanner:scanner2 | 2.825 | 2.831 | 0.007 | 0.234 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7
dataset: thickness | sex: m | region: smri_thick_cdk_cdmdfrlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -12255.3 | -12227.97 | 6131.651 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -12363.7 | -12309.03 | 6189.848 | 1 vs 2 | 116.3952 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -10228.63 | -10201.29 | 5118.314 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -10220.79 | -10166.12 | 5118.396 | 1 vs 2 | 0.163817 | 0.9968232 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 2.779 | 0.132 |
| brain_metric | scanner2 | 1808 | 2.829 | 0.144 |
| brain_metric | scanner3 | 196 | 2.756 | 0.129 |
| brain_metric | scanner5 | 2016 | 2.841 | 0.111 |
| brain_metric | scanner6 | 2264 | 2.848 | 0.121 |
| brain_metric.combat | scanner1 | 579 | 2.834 | 0.138 |
| brain_metric.combat | scanner2 | 1808 | 2.835 | 0.141 |
| brain_metric.combat | scanner3 | 196 | 2.817 | 0.133 |
| brain_metric.combat | scanner5 | 2016 | 2.828 | 0.131 |
| brain_metric.combat | scanner6 | 2264 | 2.835 | 0.137 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -0.014 | |||
| age_slope | -0.014 | |||
| scanner:scanner3 | 2.769 | 2.827 | 0.058 | 2.097 |
| scanner:scanner1 | 2.777 | 2.833 | 0.056 | 2.003 |
| scanner:scanner6 | 2.844 | 2.831 | -0.013 | -0.472 |
| scanner:scanner5 | 2.844 | 2.831 | -0.012 | -0.437 |
| scanner:scanner2 | 2.825 | 2.831 | 0.007 | 0.236 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7
dataset: thickness | sex: m | region: smri_thick_cdk_cdmdfrlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -12255.3 | -12227.97 | 6131.651 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -12363.7 | -12309.03 | 6189.848 | 1 vs 2 | 116.3952 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | -10224.97 | -10197.63 | 5116.483 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | -10217.03 | -10162.36 | 5116.513 | 1 vs 2 | 0.0608185 | 0.9995469 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 2.779 | 0.132 |
| brain_metric | scanner2 | 1808 | 2.829 | 0.144 |
| brain_metric | scanner3 | 196 | 2.756 | 0.129 |
| brain_metric | scanner5 | 2016 | 2.841 | 0.111 |
| brain_metric | scanner6 | 2264 | 2.848 | 0.121 |
| brain_metric.combat | scanner1 | 579 | 2.833 | 0.138 |
| brain_metric.combat | scanner2 | 1808 | 2.835 | 0.141 |
| brain_metric.combat | scanner3 | 196 | 2.819 | 0.133 |
| brain_metric.combat | scanner5 | 2016 | 2.828 | 0.131 |
| brain_metric.combat | scanner6 | 2264 | 2.834 | 0.137 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -0.014 | |||
| age_slope | -0.014 | |||
| scanner:scanner3 | 2.769 | 2.829 | 0.060 | 2.166 |
| scanner:scanner1 | 2.777 | 2.832 | 0.055 | 1.979 |
| scanner:scanner6 | 2.844 | 2.831 | -0.014 | -0.478 |
| scanner:scanner5 | 2.844 | 2.831 | -0.012 | -0.428 |
| scanner:scanner2 | 2.825 | 2.831 | 0.007 | 0.234 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
area — f
[longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6
dataset: area | sex: f | region: smri_area_cdk_mdtmlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 82923.34 | 82950.14 | -41457.67 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 82778.44 | 82832.04 | -41381.22 | 1 vs 2 | 152.9054 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 84702.09 | 84728.89 | -42347.05 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 84709.40 | 84763.00 | -42346.70 | 1 vs 2 | 0.6961797 | 0.9517992 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 3449.066 | 402.696 |
| brain_metric | scanner2 | 1720 | 3640.774 | 451.854 |
| brain_metric | scanner3 | 204 | 3349.922 | 398.007 |
| brain_metric | scanner5 | 1596 | 3478.284 | 413.083 |
| brain_metric | scanner6 | 2015 | 3463.817 | 412.676 |
| brain_metric.combat | scanner1 | 469 | 3542.788 | 422.232 |
| brain_metric.combat | scanner2 | 1720 | 3509.192 | 448.405 |
| brain_metric.combat | scanner3 | 204 | 3508.632 | 418.113 |
| brain_metric.combat | scanner5 | 1596 | 3509.699 | 427.640 |
| brain_metric.combat | scanner6 | 2015 | 3513.268 | 437.272 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -11.067 | |||
| age_slope | -6.562 | |||
| scanner:scanner3 | 3350.180 | 3511.397 | 161.217 | 4.812 |
| scanner:scanner2 | 3640.186 | 3510.750 | -129.436 | -3.556 |
| scanner:scanner1 | 3414.452 | 3504.021 | 89.568 | 2.623 |
| scanner:scanner6 | 3459.880 | 3512.040 | 52.160 | 1.508 |
| scanner:scanner5 | 3473.810 | 3502.206 | 28.396 | 0.817 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: area | sex: f | region: smri_area_cdk_mdtmlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 82923.34 | 82950.14 | -41457.67 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 82778.44 | 82832.04 | -41381.22 | 1 vs 2 | 152.9054 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 84700.83 | 84727.64 | -42346.42 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 84708.30 | 84761.90 | -42346.15 | 1 vs 2 | 0.5327495 | 0.9702355 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 3449.066 | 402.696 |
| brain_metric | scanner2 | 1720 | 3640.774 | 451.854 |
| brain_metric | scanner3 | 204 | 3349.922 | 398.007 |
| brain_metric | scanner5 | 1596 | 3478.284 | 413.083 |
| brain_metric | scanner6 | 2015 | 3463.817 | 412.676 |
| brain_metric.combat | scanner1 | 469 | 3545.002 | 422.183 |
| brain_metric.combat | scanner2 | 1720 | 3508.670 | 448.402 |
| brain_metric.combat | scanner3 | 204 | 3504.084 | 417.978 |
| brain_metric.combat | scanner5 | 1596 | 3510.262 | 427.653 |
| brain_metric.combat | scanner6 | 2015 | 3513.212 | 437.232 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -11.067 | |||
| age_slope | -6.506 | |||
| scanner:scanner3 | 3350.180 | 3506.499 | 156.319 | 4.666 |
| scanner:scanner2 | 3640.186 | 3510.207 | -129.978 | -3.571 |
| scanner:scanner1 | 3414.452 | 3506.530 | 92.078 | 2.697 |
| scanner:scanner6 | 3459.880 | 3511.809 | 51.929 | 1.501 |
| scanner:scanner5 | 3473.810 | 3502.994 | 29.184 | 0.840 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: area | sex: f | region: smri_area_cdk_mdtmlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 82923.34 | 82950.14 | -41457.67 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 82778.44 | 82832.04 | -41381.22 | 1 vs 2 | 152.9054 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 84702.05 | 84728.85 | -42347.02 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 84709.37 | 84762.97 | -42346.69 | 1 vs 2 | 0.6736663 | 0.9545362 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 3449.066 | 402.696 |
| brain_metric | scanner2 | 1720 | 3640.774 | 451.854 |
| brain_metric | scanner3 | 204 | 3349.922 | 398.007 |
| brain_metric | scanner5 | 1596 | 3478.284 | 413.083 |
| brain_metric | scanner6 | 2015 | 3463.817 | 412.676 |
| brain_metric.combat | scanner1 | 469 | 3543.958 | 422.249 |
| brain_metric.combat | scanner2 | 1720 | 3508.953 | 448.405 |
| brain_metric.combat | scanner3 | 204 | 3506.901 | 418.066 |
| brain_metric.combat | scanner5 | 1596 | 3509.757 | 427.639 |
| brain_metric.combat | scanner6 | 2015 | 3513.328 | 437.265 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -11.067 | |||
| age_slope | -6.553 | |||
| scanner:scanner3 | 3350.180 | 3509.531 | 159.351 | 4.756 |
| scanner:scanner2 | 3640.186 | 3510.494 | -129.692 | -3.563 |
| scanner:scanner1 | 3414.452 | 3505.359 | 90.907 | 2.662 |
| scanner:scanner6 | 3459.880 | 3512.099 | 52.219 | 1.509 |
| scanner:scanner5 | 3473.810 | 3502.241 | 28.431 | 0.818 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7
dataset: area | sex: f | region: smri_area_cdk_mdtmlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 82923.34 | 82950.14 | -41457.67 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 82778.44 | 82832.04 | -41381.22 | 1 vs 2 | 152.9054 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 84690.86 | 84717.66 | -42341.43 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 84698.18 | 84751.78 | -42341.09 | 1 vs 2 | 0.6780178 | 0.9540118 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 3449.066 | 402.696 |
| brain_metric | scanner2 | 1720 | 3640.774 | 451.854 |
| brain_metric | scanner3 | 204 | 3349.922 | 398.007 |
| brain_metric | scanner5 | 1596 | 3478.284 | 413.083 |
| brain_metric | scanner6 | 2015 | 3463.817 | 412.676 |
| brain_metric.combat | scanner1 | 469 | 3544.602 | 422.142 |
| brain_metric.combat | scanner2 | 1720 | 3508.565 | 448.352 |
| brain_metric.combat | scanner3 | 204 | 3502.533 | 417.301 |
| brain_metric.combat | scanner5 | 1596 | 3509.773 | 427.543 |
| brain_metric.combat | scanner6 | 2015 | 3513.944 | 437.663 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -11.067 | |||
| age_slope | -6.462 | |||
| scanner:scanner3 | 3350.180 | 3505.712 | 155.532 | 4.642 |
| scanner:scanner2 | 3640.186 | 3510.124 | -130.061 | -3.573 |
| scanner:scanner1 | 3414.452 | 3505.815 | 91.363 | 2.676 |
| scanner:scanner6 | 3459.880 | 3512.489 | 52.610 | 1.521 |
| scanner:scanner5 | 3473.810 | 3502.544 | 28.734 | 0.827 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7
dataset: area | sex: f | region: smri_area_cdk_mdtmlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 82923.34 | 82950.14 | -41457.67 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 82778.44 | 82832.04 | -41381.22 | 1 vs 2 | 152.9054 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 84704.75 | 84731.55 | -42348.37 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 84712.07 | 84765.67 | -42348.03 | 1 vs 2 | 0.6797435 | 0.9538033 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 3449.066 | 402.696 |
| brain_metric | scanner2 | 1720 | 3640.774 | 451.854 |
| brain_metric | scanner3 | 204 | 3349.922 | 398.007 |
| brain_metric | scanner5 | 1596 | 3478.284 | 413.083 |
| brain_metric | scanner6 | 2015 | 3463.817 | 412.676 |
| brain_metric.combat | scanner1 | 469 | 3544.577 | 422.383 |
| brain_metric.combat | scanner2 | 1720 | 3509.098 | 448.447 |
| brain_metric.combat | scanner3 | 204 | 3506.549 | 418.049 |
| brain_metric.combat | scanner5 | 1596 | 3509.571 | 427.648 |
| brain_metric.combat | scanner6 | 2015 | 3513.244 | 437.249 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | -11.067 | |||
| age_slope | -6.544 | |||
| scanner:scanner3 | 3350.180 | 3509.334 | 159.153 | 4.751 |
| scanner:scanner2 | 3640.186 | 3510.580 | -129.605 | -3.560 |
| scanner:scanner1 | 3414.452 | 3505.644 | 91.192 | 2.671 |
| scanner:scanner6 | 3459.880 | 3512.063 | 52.183 | 1.508 |
| scanner:scanner5 | 3473.810 | 3502.153 | 28.343 | 0.816 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
area — m
[longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 71 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] fitting lme model for feature 43 [longCombat] fitting lme model for feature 44 [longCombat] fitting lme model for feature 45 [longCombat] fitting lme model for feature 46 [longCombat] fitting lme model for feature 47 [longCombat] fitting lme model for feature 48 [longCombat] fitting lme model for feature 49 [longCombat] fitting lme model for feature 50 [longCombat] fitting lme model for feature 51 [longCombat] fitting lme model for feature 52 [longCombat] fitting lme model for feature 53 [longCombat] fitting lme model for feature 54 [longCombat] fitting lme model for feature 55 [longCombat] fitting lme model for feature 56 [longCombat] fitting lme model for feature 57 [longCombat] fitting lme model for feature 58 [longCombat] fitting lme model for feature 59 [longCombat] fitting lme model for feature 60 [longCombat] fitting lme model for feature 61 [longCombat] fitting lme model for feature 62 [longCombat] fitting lme model for feature 63 [longCombat] fitting lme model for feature 64 [longCombat] fitting lme model for feature 65 [longCombat] fitting lme model for feature 66 [longCombat] fitting lme model for feature 67 [longCombat] fitting lme model for feature 68 [longCombat] fitting lme model for feature 69 [longCombat] fitting lme model for feature 70 [longCombat] fitting lme model for feature 71 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6
dataset: area | sex: m | region: smri_area_cdk_mdtmlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 95185.14 | 95212.48 | -47588.57 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 94902.78 | 94957.45 | -47443.39 | 1 vs 2 | 290.3618 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 97129.24 | 97156.58 | -48560.62 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 97137.02 | 97191.69 | -48560.51 | 1 vs 2 | 0.2276452 | 0.9939934 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 3755.839 | 462.895 |
| brain_metric | scanner2 | 1808 | 4037.319 | 486.071 |
| brain_metric | scanner3 | 196 | 3646.485 | 514.494 |
| brain_metric | scanner5 | 2016 | 3792.857 | 445.779 |
| brain_metric | scanner6 | 2264 | 3833.975 | 449.369 |
| brain_metric.combat | scanner1 | 579 | 3880.490 | 477.411 |
| brain_metric.combat | scanner2 | 1808 | 3851.263 | 479.856 |
| brain_metric.combat | scanner3 | 196 | 3866.100 | 526.370 |
| brain_metric.combat | scanner5 | 2016 | 3843.197 | 467.669 |
| brain_metric.combat | scanner6 | 2264 | 3886.699 | 469.732 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 6.835 | |||
| age_slope | 8.893 | |||
| scanner:scanner3 | 3646.649 | 3878.281 | 231.632 | 6.352 |
| scanner:scanner2 | 4051.359 | 3860.821 | -190.538 | -4.703 |
| scanner:scanner1 | 3737.765 | 3864.961 | 127.196 | 3.403 |
| scanner:scanner6 | 3812.994 | 3866.440 | 53.446 | 1.402 |
| scanner:scanner5 | 3811.261 | 3863.904 | 52.644 | 1.381 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: area | sex: m | region: smri_area_cdk_mdtmlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 95185.14 | 95212.48 | -47588.57 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 94902.78 | 94957.45 | -47443.39 | 1 vs 2 | 290.3618 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 97128.27 | 97155.60 | -48560.13 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 97136.11 | 97190.78 | -48560.05 | 1 vs 2 | 0.1589453 | 0.9970045 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 3755.839 | 462.895 |
| brain_metric | scanner2 | 1808 | 4037.319 | 486.071 |
| brain_metric | scanner3 | 196 | 3646.485 | 514.494 |
| brain_metric | scanner5 | 2016 | 3792.857 | 445.779 |
| brain_metric | scanner6 | 2264 | 3833.975 | 449.369 |
| brain_metric.combat | scanner1 | 579 | 3883.229 | 477.432 |
| brain_metric.combat | scanner2 | 1808 | 3850.960 | 479.852 |
| brain_metric.combat | scanner3 | 196 | 3860.053 | 526.315 |
| brain_metric.combat | scanner5 | 2016 | 3843.420 | 467.625 |
| brain_metric.combat | scanner6 | 2264 | 3886.566 | 469.715 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 6.835 | |||
| age_slope | 8.915 | |||
| scanner:scanner3 | 3646.649 | 3871.430 | 224.780 | 6.164 |
| scanner:scanner2 | 4051.359 | 3860.500 | -190.859 | -4.711 |
| scanner:scanner1 | 3737.765 | 3868.014 | 130.249 | 3.485 |
| scanner:scanner6 | 3812.994 | 3866.167 | 53.173 | 1.395 |
| scanner:scanner5 | 3811.261 | 3864.317 | 53.056 | 1.392 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: area | sex: m | region: smri_area_cdk_mdtmlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 95185.14 | 95212.48 | -47588.57 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 94902.78 | 94957.45 | -47443.39 | 1 vs 2 | 290.3618 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 97129.20 | 97156.54 | -48560.60 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 97136.98 | 97191.65 | -48560.49 | 1 vs 2 | 0.2211063 | 0.9943213 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 3755.839 | 462.895 |
| brain_metric | scanner2 | 1808 | 4037.319 | 486.071 |
| brain_metric | scanner3 | 196 | 3646.485 | 514.494 |
| brain_metric | scanner5 | 2016 | 3792.857 | 445.779 |
| brain_metric | scanner6 | 2264 | 3833.975 | 449.369 |
| brain_metric.combat | scanner1 | 579 | 3880.744 | 477.413 |
| brain_metric.combat | scanner2 | 1808 | 3850.934 | 479.853 |
| brain_metric.combat | scanner3 | 196 | 3865.860 | 526.351 |
| brain_metric.combat | scanner5 | 2016 | 3843.452 | 467.656 |
| brain_metric.combat | scanner6 | 2264 | 3886.691 | 469.716 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 6.835 | |||
| age_slope | 8.899 | |||
| scanner:scanner3 | 3646.649 | 3878.021 | 231.372 | 6.345 |
| scanner:scanner2 | 4051.359 | 3860.472 | -190.888 | -4.712 |
| scanner:scanner1 | 3737.765 | 3865.228 | 127.462 | 3.410 |
| scanner:scanner6 | 3812.994 | 3866.326 | 53.332 | 1.399 |
| scanner:scanner5 | 3811.261 | 3864.307 | 53.046 | 1.392 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7
dataset: area | sex: m | region: smri_area_cdk_mdtmlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 95185.14 | 95212.48 | -47588.57 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 94902.78 | 94957.45 | -47443.39 | 1 vs 2 | 290.3618 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 97110.10 | 97137.43 | -48551.05 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 97117.86 | 97172.53 | -48550.93 | 1 vs 2 | 0.2442297 | 0.9931241 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 3755.839 | 462.895 |
| brain_metric | scanner2 | 1808 | 4037.319 | 486.071 |
| brain_metric | scanner3 | 196 | 3646.485 | 514.494 |
| brain_metric | scanner5 | 2016 | 3792.857 | 445.779 |
| brain_metric | scanner6 | 2264 | 3833.975 | 449.369 |
| brain_metric.combat | scanner1 | 579 | 3883.105 | 477.438 |
| brain_metric.combat | scanner2 | 1808 | 3850.890 | 479.864 |
| brain_metric.combat | scanner3 | 196 | 3859.114 | 526.563 |
| brain_metric.combat | scanner5 | 2016 | 3842.187 | 467.359 |
| brain_metric.combat | scanner6 | 2264 | 3887.836 | 469.788 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 6.835 | |||
| age_slope | 8.892 | |||
| scanner:scanner3 | 3646.649 | 3871.565 | 224.916 | 6.168 |
| scanner:scanner2 | 4051.359 | 3860.576 | -190.783 | -4.709 |
| scanner:scanner1 | 3737.765 | 3867.730 | 129.964 | 3.477 |
| scanner:scanner6 | 3812.994 | 3867.089 | 54.095 | 1.419 |
| scanner:scanner5 | 3811.261 | 3863.281 | 52.020 | 1.365 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7
dataset: area | sex: m | region: smri_area_cdk_mdtmlh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 95185.14 | 95212.48 | -47588.57 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 94902.78 | 94957.45 | -47443.39 | 1 vs 2 | 290.3618 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 97129.59 | 97156.92 | -48560.79 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 97137.36 | 97192.03 | -48560.68 | 1 vs 2 | 0.2306247 | 0.9938412 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 3755.839 | 462.895 |
| brain_metric | scanner2 | 1808 | 4037.319 | 486.071 |
| brain_metric | scanner3 | 196 | 3646.485 | 514.494 |
| brain_metric | scanner5 | 2016 | 3792.857 | 445.779 |
| brain_metric | scanner6 | 2264 | 3833.975 | 449.369 |
| brain_metric.combat | scanner1 | 579 | 3880.444 | 477.540 |
| brain_metric.combat | scanner2 | 1808 | 3850.944 | 479.892 |
| brain_metric.combat | scanner3 | 196 | 3866.880 | 526.347 |
| brain_metric.combat | scanner5 | 2016 | 3843.384 | 467.621 |
| brain_metric.combat | scanner6 | 2264 | 3886.732 | 469.679 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 6.835 | |||
| age_slope | 8.888 | |||
| scanner:scanner3 | 3646.649 | 3878.740 | 232.091 | 6.365 |
| scanner:scanner2 | 4051.359 | 3860.543 | -190.816 | -4.710 |
| scanner:scanner1 | 3737.765 | 3865.058 | 127.292 | 3.406 |
| scanner:scanner6 | 3812.994 | 3866.321 | 53.328 | 1.399 |
| scanner:scanner5 | 3811.261 | 3864.217 | 52.956 | 1.389 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
volume — f
## longcombat is dropping 2 feature(s) with NA.[longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6004 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6
dataset: volume | sex: f | region: smri_vol_scs_amygdalarh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 75803.15 | 75829.95 | -37897.57 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 75746.13 | 75799.73 | -37865.07 | 1 vs 2 | 65.01632 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 77717.92 | 77744.72 | -38854.96 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 77725.84 | 77779.45 | -38854.92 | 1 vs 2 | 0.0717721 | 0.9993713 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 1792.106 | 188.323 |
| brain_metric | scanner2 | 1720 | 1726.334 | 191.118 |
| brain_metric | scanner3 | 204 | 1781.268 | 183.449 |
| brain_metric | scanner5 | 1596 | 1782.956 | 188.630 |
| brain_metric | scanner6 | 2015 | 1775.741 | 188.518 |
| brain_metric.combat | scanner1 | 469 | 1772.467 | 197.977 |
| brain_metric.combat | scanner2 | 1720 | 1767.412 | 200.762 |
| brain_metric.combat | scanner3 | 204 | 1765.441 | 190.134 |
| brain_metric.combat | scanner5 | 1596 | 1760.986 | 198.590 |
| brain_metric.combat | scanner6 | 2015 | 1764.321 | 198.678 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 8.034 | |||
| age_slope | 8.112 | |||
| scanner:scanner2 | 1723.371 | 1764.659 | 41.288 | 2.396 |
| scanner:scanner5 | 1785.131 | 1763.086 | -22.045 | -1.235 |
| scanner:scanner1 | 1783.755 | 1766.018 | -17.736 | -0.994 |
| scanner:scanner3 | 1780.944 | 1765.092 | -15.852 | -0.890 |
| scanner:scanner6 | 1776.311 | 1764.380 | -11.931 | -0.672 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: volume | sex: f | region: smri_vol_scs_amygdalarh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 75803.15 | 75829.95 | -37897.57 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 75746.13 | 75799.73 | -37865.07 | 1 vs 2 | 65.01632 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 77717.62 | 77744.42 | -38854.81 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 77725.55 | 77779.15 | -38854.77 | 1 vs 2 | 0.0698744 | 0.9994037 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 1792.106 | 188.323 |
| brain_metric | scanner2 | 1720 | 1726.334 | 191.118 |
| brain_metric | scanner3 | 204 | 1781.268 | 183.449 |
| brain_metric | scanner5 | 1596 | 1782.956 | 188.630 |
| brain_metric | scanner6 | 2015 | 1775.741 | 188.518 |
| brain_metric.combat | scanner1 | 469 | 1773.020 | 197.982 |
| brain_metric.combat | scanner2 | 1720 | 1767.292 | 200.756 |
| brain_metric.combat | scanner3 | 204 | 1764.442 | 190.136 |
| brain_metric.combat | scanner5 | 1596 | 1761.164 | 198.585 |
| brain_metric.combat | scanner6 | 2015 | 1764.255 | 198.678 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 8.034 | |||
| age_slope | 8.125 | |||
| scanner:scanner2 | 1723.371 | 1764.540 | 41.170 | 2.389 |
| scanner:scanner5 | 1785.131 | 1763.285 | -21.846 | -1.224 |
| scanner:scanner1 | 1783.755 | 1766.584 | -17.170 | -0.963 |
| scanner:scanner3 | 1780.944 | 1764.081 | -16.863 | -0.947 |
| scanner:scanner6 | 1776.311 | 1764.301 | -12.010 | -0.676 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: volume | sex: f | region: smri_vol_scs_amygdalarh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 75803.15 | 75829.95 | -37897.57 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 75746.13 | 75799.73 | -37865.07 | 1 vs 2 | 65.01632 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 77717.43 | 77744.23 | -38854.71 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 77725.33 | 77778.93 | -38854.67 | 1 vs 2 | 0.0946972 | 0.9989138 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 1792.106 | 188.323 |
| brain_metric | scanner2 | 1720 | 1726.334 | 191.118 |
| brain_metric | scanner3 | 204 | 1781.268 | 183.449 |
| brain_metric | scanner5 | 1596 | 1782.956 | 188.630 |
| brain_metric | scanner6 | 2015 | 1775.741 | 188.518 |
| brain_metric.combat | scanner1 | 469 | 1773.329 | 197.979 |
| brain_metric.combat | scanner2 | 1720 | 1767.248 | 200.755 |
| brain_metric.combat | scanner3 | 204 | 1764.267 | 190.132 |
| brain_metric.combat | scanner5 | 1596 | 1761.015 | 198.579 |
| brain_metric.combat | scanner6 | 2015 | 1764.356 | 198.682 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 8.034 | |||
| age_slope | 8.119 | |||
| scanner:scanner2 | 1723.371 | 1764.496 | 41.125 | 2.386 |
| scanner:scanner5 | 1785.131 | 1763.112 | -22.019 | -1.233 |
| scanner:scanner3 | 1780.944 | 1763.903 | -17.041 | -0.957 |
| scanner:scanner1 | 1783.755 | 1766.906 | -16.849 | -0.945 |
| scanner:scanner6 | 1776.311 | 1764.413 | -11.898 | -0.670 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7
dataset: volume | sex: f | region: smri_vol_scs_amygdalarh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 75803.15 | 75829.95 | -37897.57 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 75746.13 | 75799.73 | -37865.07 | 1 vs 2 | 65.01632 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 77708.42 | 77735.22 | -38850.21 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 77716.31 | 77769.91 | -38850.16 | 1 vs 2 | 0.1074109 | 0.9986085 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 1792.106 | 188.323 |
| brain_metric | scanner2 | 1720 | 1726.334 | 191.118 |
| brain_metric | scanner3 | 204 | 1781.268 | 183.449 |
| brain_metric | scanner5 | 1596 | 1782.956 | 188.630 |
| brain_metric | scanner6 | 2015 | 1775.741 | 188.518 |
| brain_metric.combat | scanner1 | 469 | 1772.498 | 197.869 |
| brain_metric.combat | scanner2 | 1720 | 1767.195 | 200.603 |
| brain_metric.combat | scanner3 | 204 | 1764.090 | 190.516 |
| brain_metric.combat | scanner5 | 1596 | 1760.712 | 198.598 |
| brain_metric.combat | scanner6 | 2015 | 1764.851 | 198.622 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 8.034 | |||
| age_slope | 8.107 | |||
| scanner:scanner2 | 1723.371 | 1764.450 | 41.079 | 2.384 |
| scanner:scanner5 | 1785.131 | 1762.874 | -22.257 | -1.247 |
| scanner:scanner1 | 1783.755 | 1766.097 | -17.658 | -0.990 |
| scanner:scanner3 | 1780.944 | 1763.712 | -17.231 | -0.968 |
| scanner:scanner6 | 1776.311 | 1764.849 | -11.462 | -0.645 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7
dataset: volume | sex: f | region: smri_vol_scs_amygdalarh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 75803.15 | 75829.95 | -37897.57 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 75746.13 | 75799.73 | -37865.07 | 1 vs 2 | 65.01632 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 77722.00 | 77748.81 | -38857.00 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 77729.91 | 77783.51 | -38856.95 | 1 vs 2 | 0.0968784 | 0.998864 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 469 | 1792.106 | 188.323 |
| brain_metric | scanner2 | 1720 | 1726.334 | 191.118 |
| brain_metric | scanner3 | 204 | 1781.268 | 183.449 |
| brain_metric | scanner5 | 1596 | 1782.956 | 188.630 |
| brain_metric | scanner6 | 2015 | 1775.741 | 188.518 |
| brain_metric.combat | scanner1 | 469 | 1773.401 | 198.054 |
| brain_metric.combat | scanner2 | 1720 | 1767.257 | 200.774 |
| brain_metric.combat | scanner3 | 204 | 1764.213 | 190.099 |
| brain_metric.combat | scanner5 | 1596 | 1761.009 | 198.602 |
| brain_metric.combat | scanner6 | 2015 | 1764.341 | 198.732 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 8.034 | |||
| age_slope | 8.120 | |||
| scanner:scanner2 | 1723.371 | 1764.508 | 41.137 | 2.387 |
| scanner:scanner5 | 1785.131 | 1763.110 | -22.021 | -1.234 |
| scanner:scanner3 | 1780.944 | 1763.856 | -17.088 | -0.960 |
| scanner:scanner1 | 1783.755 | 1766.966 | -16.788 | -0.941 |
| scanner:scanner6 | 1776.311 | 1764.395 | -11.916 | -0.671 |
| scanner:scanner7 | ||||
| scanner:scanner8 |
volume — m
## longcombat is dropping 2 feature(s) with NA.[longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects [longCombat] found 5 batches [longCombat] found 42 features [longCombat] found 6863 total observations [longCombat] standardizing data across features… [longCombat] fitting lme model for feature 1 [longCombat] fitting lme model for feature 2 [longCombat] fitting lme model for feature 3 [longCombat] fitting lme model for feature 4 [longCombat] fitting lme model for feature 5 [longCombat] fitting lme model for feature 6 [longCombat] fitting lme model for feature 7 [longCombat] fitting lme model for feature 8 [longCombat] fitting lme model for feature 9 [longCombat] fitting lme model for feature 10 [longCombat] fitting lme model for feature 11 [longCombat] fitting lme model for feature 12 [longCombat] fitting lme model for feature 13 [longCombat] fitting lme model for feature 14 [longCombat] fitting lme model for feature 15 [longCombat] fitting lme model for feature 16 [longCombat] fitting lme model for feature 17 [longCombat] fitting lme model for feature 18 [longCombat] fitting lme model for feature 19 [longCombat] fitting lme model for feature 20 [longCombat] fitting lme model for feature 21 [longCombat] fitting lme model for feature 22 [longCombat] fitting lme model for feature 23 [longCombat] fitting lme model for feature 24 [longCombat] fitting lme model for feature 25 [longCombat] fitting lme model for feature 26 [longCombat] fitting lme model for feature 27 [longCombat] fitting lme model for feature 28 [longCombat] fitting lme model for feature 29 [longCombat] fitting lme model for feature 30 [longCombat] fitting lme model for feature 31 [longCombat] fitting lme model for feature 32 [longCombat] fitting lme model for feature 33 [longCombat] fitting lme model for feature 34 [longCombat] fitting lme model for feature 35 [longCombat] fitting lme model for feature 36 [longCombat] fitting lme model for feature 37 [longCombat] fitting lme model for feature 38 [longCombat] fitting lme model for feature 39 [longCombat] fitting lme model for feature 40 [longCombat] fitting lme model for feature 41 [longCombat] fitting lme model for feature 42 [longCombat] using method of moments to estimate hyperparameters [longCombat] using empirical Bayes to estimate batch effects… [longCombat] initializing… [longCombat] starting EM algorithm iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat] starting EM algorithm iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat] starting EM algorithm iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat] starting EM algorithm iteration 10 [longCombat] starting EM algorithm iteration 11 [longCombat] starting EM algorithm iteration 12 [longCombat] starting EM algorithm iteration 13 [longCombat] starting EM algorithm iteration 14 [longCombat] starting EM algorithm iteration 15 [longCombat] starting EM algorithm iteration 16 [longCombat] starting EM algorithm iteration 17 [longCombat] starting EM algorithm iteration 18 [longCombat] starting EM algorithm iteration 19 [longCombat] starting EM algorithm iteration 20 [longCombat] starting EM algorithm iteration 21 [longCombat] starting EM algorithm iteration 22 [longCombat] starting EM algorithm iteration 23 [longCombat] starting EM algorithm iteration 24 [longCombat] starting EM algorithm iteration 25 [longCombat] starting EM algorithm iteration 26 [longCombat] starting EM algorithm iteration 27 [longCombat] starting EM algorithm iteration 28 [longCombat] starting EM algorithm iteration 29 [longCombat] starting EM algorithm iteration 30 [longCombat] adjusting data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6
dataset: volume | sex: m | region: smri_vol_scs_amygdalarh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 87765.12 | 87792.46 | -43878.56 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 87676.81 | 87731.48 | -43830.40 | 1 vs 2 | 96.31319 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 89945.34 | 89972.68 | -44968.67 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 89953.18 | 90007.85 | -44968.59 | 1 vs 2 | 0.1670111 | 0.9967016 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 1957.793 | 226.961 |
| brain_metric | scanner2 | 1808 | 1887.890 | 210.181 |
| brain_metric | scanner3 | 196 | 1924.909 | 208.734 |
| brain_metric | scanner5 | 2016 | 1951.739 | 206.193 |
| brain_metric | scanner6 | 2264 | 1953.379 | 211.401 |
| brain_metric.combat | scanner1 | 579 | 1926.908 | 234.271 |
| brain_metric.combat | scanner2 | 1808 | 1940.706 | 223.463 |
| brain_metric.combat | scanner3 | 196 | 1944.029 | 215.666 |
| brain_metric.combat | scanner5 | 2016 | 1928.233 | 215.624 |
| brain_metric.combat | scanner6 | 2264 | 1938.445 | 221.035 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 21.828 | |||
| age_slope | 21.880 | |||
| scanner:scanner2 | 1881.675 | 1935.396 | 53.721 | 2.855 |
| scanner:scanner1 | 1965.306 | 1932.062 | -33.243 | -1.692 |
| scanner:scanner5 | 1957.986 | 1933.281 | -24.705 | -1.262 |
| scanner:scanner3 | 1914.673 | 1934.310 | 19.637 | 1.026 |
| scanner:scanner6 | 1949.493 | 1935.556 | -13.938 | -0.715 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: volume | sex: m | region: smri_vol_scs_amygdalarh
Distributions
Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: volume | sex: m | region: smri_vol_scs_amygdalarh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 87765.12 | 87792.46 | -43878.56 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 87676.81 | 87731.48 | -43830.40 | 1 vs 2 | 96.31319 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 89945.36 | 89972.69 | -44968.68 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 89953.22 | 90007.89 | -44968.61 | 1 vs 2 | 0.1416313 | 0.9976079 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 1957.793 | 226.961 |
| brain_metric | scanner2 | 1808 | 1887.890 | 210.181 |
| brain_metric | scanner3 | 196 | 1924.909 | 208.734 |
| brain_metric | scanner5 | 2016 | 1951.739 | 206.193 |
| brain_metric | scanner6 | 2264 | 1953.379 | 211.401 |
| brain_metric.combat | scanner1 | 579 | 1926.996 | 234.273 |
| brain_metric.combat | scanner2 | 1808 | 1940.587 | 223.461 |
| brain_metric.combat | scanner3 | 196 | 1943.951 | 215.669 |
| brain_metric.combat | scanner5 | 2016 | 1928.369 | 215.626 |
| brain_metric.combat | scanner6 | 2264 | 1938.403 | 221.032 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 21.828 | |||
| age_slope | 21.882 | |||
| scanner:scanner2 | 1881.675 | 1935.275 | 53.600 | 2.849 |
| scanner:scanner1 | 1965.306 | 1932.151 | -33.155 | -1.687 |
| scanner:scanner5 | 1957.986 | 1933.432 | -24.554 | -1.254 |
| scanner:scanner3 | 1914.673 | 1934.237 | 19.564 | 1.022 |
| scanner:scanner6 | 1949.493 | 1935.505 | -13.989 | -0.718 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 + s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 + s_age_by_timing_6 + s_age_by_timing_7
dataset: volume | sex: m | region: smri_vol_scs_amygdalarh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 87765.12 | 87792.46 | -43878.56 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 87676.81 | 87731.48 | -43830.40 | 1 vs 2 | 96.31319 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 89945.38 | 89972.71 | -44968.69 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 89953.18 | 90007.85 | -44968.59 | 1 vs 2 | 0.1991848 | 0.995358 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 1957.793 | 226.961 |
| brain_metric | scanner2 | 1808 | 1887.890 | 210.181 |
| brain_metric | scanner3 | 196 | 1924.909 | 208.734 |
| brain_metric | scanner5 | 2016 | 1951.739 | 206.193 |
| brain_metric | scanner6 | 2264 | 1953.379 | 211.401 |
| brain_metric.combat | scanner1 | 579 | 1927.357 | 234.012 |
| brain_metric.combat | scanner2 | 1808 | 1940.684 | 223.418 |
| brain_metric.combat | scanner3 | 196 | 1942.564 | 216.057 |
| brain_metric.combat | scanner5 | 2016 | 1928.022 | 215.698 |
| brain_metric.combat | scanner6 | 2264 | 1938.660 | 221.047 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 21.828 | |||
| age_slope | 21.871 | |||
| scanner:scanner2 | 1881.675 | 1935.395 | 53.720 | 2.855 |
| scanner:scanner1 | 1965.306 | 1932.469 | -32.836 | -1.671 |
| scanner:scanner5 | 1957.986 | 1933.091 | -24.895 | -1.271 |
| scanner:scanner3 | 1914.673 | 1932.895 | 18.222 | 0.952 |
| scanner:scanner6 | 1949.493 | 1935.743 | -13.751 | -0.705 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 + s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 + s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 + s_age_by_tempo_6 + s_age_by_tempo_7
dataset: volume | sex: m | region: smri_vol_scs_amygdalarh
Distributions
Scanner LRTs
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0 | lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 87765.12 | 87792.46 | -43878.56 | |||
| m1 | lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 87676.81 | 87731.48 | -43830.40 | 1 vs 2 | 96.31319 | 0 |
| call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
|---|---|---|---|---|---|---|---|---|---|
| m0c | lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 1 | 4 | 89947.24 | 89974.58 | -44969.62 | |||
| m1c | lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data = mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit) | 2 | 8 | 89955.08 | 90009.75 | -44969.54 | 1 vs 2 | 0.1606397 | 0.996942 |
QC Plots and Summaries
| pipeline | scanner | n | mean | sd |
|---|---|---|---|---|
| brain_metric | scanner1 | 579 | 1957.793 | 226.961 |
| brain_metric | scanner2 | 1808 | 1887.890 | 210.181 |
| brain_metric | scanner3 | 196 | 1924.909 | 208.734 |
| brain_metric | scanner5 | 2016 | 1951.739 | 206.193 |
| brain_metric | scanner6 | 2264 | 1953.379 | 211.401 |
| brain_metric.combat | scanner1 | 579 | 1927.115 | 234.325 |
| brain_metric.combat | scanner2 | 1808 | 1940.618 | 223.493 |
| brain_metric.combat | scanner3 | 196 | 1943.393 | 215.638 |
| brain_metric.combat | scanner5 | 2016 | 1928.239 | 215.659 |
| brain_metric.combat | scanner6 | 2264 | 1938.511 | 221.006 |
| kind | Value_pre | Value_post | diff | pctchg |
|---|---|---|---|---|
| age_slope | 21.828 | |||
| age_slope | 21.885 | |||
| scanner:scanner2 | 1881.675 | 1935.280 | 53.605 | 2.849 |
| scanner:scanner1 | 1965.306 | 1932.279 | -33.027 | -1.680 |
| scanner:scanner5 | 1957.986 | 1933.311 | -24.675 | -1.260 |
| scanner:scanner3 | 1914.673 | 1933.715 | 19.043 | 0.995 |
| scanner:scanner6 | 1949.493 | 1935.621 | -13.873 | -0.712 |
| scanner:scanner4 | ||||
| scanner:scanner7 | ||||
| scanner:scanner8 |